Archives For complexity

[The following is a guest post from Philip Hanspach of the European University Institute.]

There is an emerging debate regarding whether complexity theory—which, among other things, draws lessons about uncertainty and non-linearity from the natural sciences—should make inroads into antitrust (see, e.g., Nicolas Petit and Thibault Schrepel, 2022). Of course, one might also say that antitrust is already quite late to the party. Since the 1990s, complexity theory has made inroads into numerous “hard” and social sciences, from geography and urban planning to cultural studies.

Depending on whom you ask, complexity theory is everything from a revolutionary paradigm to a lazy buzzword. What would it mean to apply it in the context of antitrust and would it, in fact, be useful?

Given its numerous applications, scholars have proposed several definitions of complexity theory, invoking different kinds of complexity. According to one, complexity theory is concerned with the study of complex adaptive systems (CAS)—that is, networks that consist of many diverse, interdependent parts. A CAS may adapt and change, for example, in response to past experience.

That does not sound too strange as a general description either of the economy as a whole or of markets in particular, with consumers, firms, and potential entrants among the numerous moving parts. At the same time, this approach contrasts with orthodox economic theory—specifically, with the game-theory models that rule antitrust debates and that prize simplicity and reductionism.

As both a competition economist and a history buff, my primary point of reference for complexity theory is a scholarly debate among Bronze Age scholars. Sound obscure? Bear with me.

The collapse of several flourishing Mediterranean civilizations in the 12th century B.C. (Mycenae and Egypt, to name only two) puzzles historians as much as today’s economists are stumped by the question of whether any particular merger will raise prices.[1] Both questions encounter difficulties in gathering sufficient data for empirical analysis (the lack of counterfactuals and foresight in one case, and 3,000 years of decay in the other), forcing a recourse to theory and possibility results.

Earlier Bronze Age scholarship blamed the “Sea Peoples,” invaders of unknown origin (possibly Sicily or Sardinia), for the destruction of several thriving cities and states. The primary source for this thesis was statements attributed to the Egyptian pharaoh of the time. More recent research, while acknowledging the role of the Sea Peoples, but has gone to lengths to point out that, in many cases, we simply don’t know. Alternative explanations (famine, disease, systems collapse) are individually unconvincing as alternative explanations, but might each have contributed to the end of various Bronze Age civilizations.

Complexity theory was brought into this discussion with some caution. While acknowledging the theory’s potential usefulness, Eric Cline writes:

We may just be applying a scientific (or possibly pseudoscientific) term to a situation in which there is insufficient knowledge to draw firm conclusions. It sounds nice, but does it really advance our understanding? Is it more than just a fancy way to state a fairly obvious fact?

In a review of Cline’s book, archaeologist Guy D. Middleton agreed that the application of complexity theory might be “useful” but also “obvious.” Similarly, in the context of antitrust, I think complexity theory may serve as a useful framework to understand uncertainty in the marketplace.

Thinking of a market as a CAS can help to illustrate the uncertainty behind every decision. For example, a formal economic model with a clear (at least, to economists) equilibrium outcome might predict that a certain merger will give firms the incentive and ability to reduce spending on research and development. But the lens of complexity theory allows us to better understand why we might still be wrong, or why we are right, but for the wrong reasons.

We can accept that decisions that are relevant and observable to antitrust practitioners (such as price and production decisions) can be driven by things that are small and unobservable. For example, a manager who ultimately calls the shots on R&D budgets for an airplane manufacturer might go to a trade fair and become fascinated by a cool robot that a particular shipyard presented. This might have been the key push that prompted her to finance an unlikely robotics project proposed by her head engineer.

Her firm is, indeed, part of a complex system—one that includes the individual purchase decisions of consumers, customer feedback, reports from salespeople in the field, news from science and business journalists about the next big thing, and impressions at trade fairs and exhibitions. These all coalesce in the manager’s head and influence simple decisions about her R&D budget. But I have yet to see a merger-review decision that predicted effects on innovation from peeking into managers’ minds in such a way.

This little story might be a far-fetched example of the Butterfly Effect, perhaps the most familiar concept from complexity theory. Just as the flaps of a butterfly’s wings might cause a storm on the other side of the world, the shipyard’s earlier decision to invest in a robotic manufacturing technology resulted in our fictitious aircraft manufacturer’s decision to invest more in R&D than we might have predicted with our traditional tools.

Indeed, it is easy to think of other small events that can have consequences leading to price changes that are relevant in the antitrust arena. Remember the cargo ship Ever Given, which blocked the Suez Canal in March 2021? One reason mentioned for its distress were unusually strong winds (whether a butterfly was to blame, I don’t know) pushing the highly stacked containers like a sail. The disruption to supply chains was felt in various markets across Europe.

In my opinion, one benefit of admitting this complexity is that it can make ex post evaluation more common in antitrust. Indeed, some researchers are doing great work on this. Enforcers are understandably hesitant to admit that they might get it wrong sometimes, but I believe that we can acknowledge that we will not ultimately know whether merged firms will, say, invest more or less in innovation. Complexity theory tells us that, even if our best and most appropriate model is wrong, the world is not random. It is just very hard to understand and hinges on things that are neither straightforward to observe, nor easy to correctly gauge ex ante.

Turning back to the Bronze Age, scholars have an easier time observing that a certain city was destroyed and abandoned at some point in time than they do in correctly naming the culprit (the Sea Peoples, a rival power, an earthquake?) The appeal of complexity theory is not just that it lifts a scholar’s burden to name one or a few predominant explanations, but that it grants confidence that the decision itself arose out of a complex system: the big and small effects that factors such as famine, trade, weather, and fortune may have had on the city’s ability to defend itself against attack, and the individual-but-interrelated decisions of a city’s citizens to stay or leave following a catastrophe.

Similarly, for antitrust experts, it is easier to observe a price increase following a merger than to correctly guess its reason. Where economists differ from archaeologists and classicists is that they don’t just study the past. They have to continue exploring the present and future. Imagine that an agency clears a merger that we would have expected not to harm competition, but it turns out, ex post, that it was a bad call. Complexity theory doesn’t just offer excuses for where reality diverged from our prediction. Instead, it can tell us whether our tools were deficient or whether we made an “honest mistake.” As investigations are always costly, it is up to the enforcer (or those setting their budget) to decide whether it makes sense to expand investigations to account for new, complex phenomena (reading the minds of R&D managers will probably remain out of the budget for the foreseeable future).

Finally, economists working on antitrust problems should not see this as belittling their role, but as a welcome frame for their work. Computing diversion ratios or modeling a complex market as a straightforward set of equations might still be the best we can do. A model that is right on average gets us closer to the right answer and is certainly preferred to having no clue what’s going on. Where we don’t have precedent to guide us, we have to resort to models that may be wrong, despite getting everything right that was under our control.

A few things that Petit and Schrepel call for are comfortably established in the economist’s toolkit. They might not, however, always be put to use where they should. Notably, there are feedback loops in dynamic models. Even in static models, it is possible to show how a change in one variable has direct and indirect (second order) effects on an outcome. The typical merger investigation is concerned with short-term effects, perhaps those materializing over the three to five years following a merger. These short-term effects may be relatively easy to approximate in a simple model. Granted, Petit and Schrepel’s article adopts a wide understanding of antitrust—including pro-competitive market regulation—but this seems like an important caveat, nonetheless.

In conclusion, complexity theory is something economists and lawyers who study markets should learn more about. It’s a fascinating research paradigm and a framework in which one can make sense of small and large causes having sometimes unpredictable effects. For antitrust practitioners, it can advance our understanding of why our predictions can fail when the tools and approaches that we use are limited. My hope is that understanding complexity will increase openness to ex-post valuation and the expectations toward antitrust enforcement (and its limits). At the same time, it is still an (economic) question of costs and benefits as to whether further complications in an antitrust investigation are worth it.


[1] A fascinating introduction that balances approachability and source work is YouTube’s Extra History series on the Bronze Age collapse.

In a recent NY Times opinion piece, Tim Wu, like Elizabeth Holmes, lionizes Steve Jobs. Like Jobs with the iPod and iPhone, and Holmes with the Theranos Edison machine, Wu tells us we must simplify the public’s experience of complex policy into a simple box with an intuitive interface. In this spirit he argues that “what the public wants from government is help with complexity,” such that “[t]his generation of progressives … must accept that simplicity and popularity are not a dumbing-down of policy.”

This argument provides remarkable insight into the complexity problems of progressive thought. Three of these are taken up below: the mismatch of comparing the work of the government to the success of Jobs; the mismatch between Wu’s telling of and Jobs’s actual success; and the latent hypocrisy in Wu’s “simplicity for me, complexity for thee” argument.

Contra Wu’s argument, we need politicians that embrace and lay bare the complexity of policy issues. Too much of our political moment is dominated by demagogues on every side of policy debates offering simple solutions to simplified accounts of complex policy issues. We need public intellectuals, and hopefully politicians as well, to make the case for complexity. Our problems are complex and solutions to them hard (and sometimes unavailing). Without leaders willing to steer into complexity, we can never have a polity able to address complexity.

I. “Good enough for government work” isn’t good enough for Jobs

As an initial matter, there is a great deal of wisdom in Wu’s recognition that the public doesn’t want complexity. As I said at the annual Silicon Flatirons conference in February, consumers don’t want a VCR with lots of dials and knobs that let them control lots of specific features—they just want the damn thing to work. And as that example is meant to highlight, once it does work, most consumers are happy to leave well enough alone (as demonstrated by millions of clocks that would continue to blink 12:00 if VCRs weren’t so 1990s).

Where Wu goes wrong, though, is that he fails to recognize that despite this desire for simplicity, for two decades VCR manufacturers designed and sold VCRs with clocks that were never set—a persistent blinking to constantly remind consumers of their own inadequacies. Had the manufacturers had any insight into the consumer desire for simplicity, all those clocks would have been used for something—anything—other than a reminder that consumers didn’t know how to set them. (Though, to their credit, these devices were designed to operate as most consumers desired without imposing any need to set the clock upon them—a model of simplicity in basic operation that allows consumers to opt-in to a more complex experience.)

If the government were populated by visionaries like Jobs, Wu’s prescription would be wise. But Jobs was a once-in-a-generation thinker. No one in a generation of VCR designers had the insight to design a VCR without a clock (or at least a clock that didn’t blink in a constant reminder of the owner’s inability to set it). And similarly few among the ranks of policy designers are likely to have his abilities, either. On the other hand, the public loves the promise of easy solutions to complex problems. Charlatans and demagogues who would cast themselves in his image, like Holmes did with Theranos, can find government posts in abundance.

Of course, in his paean to offering the public less choice, Wu, himself an oftentime designer of government policy, compares the art of policy design to the work of Jobs—not of Holmes. But where he promises a government run in the manner of Apple, he would more likely give us one more in the mold of Theranos.

There is a more pernicious side to Wu’s argument. He speaks of respect for the public, arguing that “Real respect for the public involves appreciating what the public actually wants and needs,” and that “They would prefer that the government solve problems for them.” Another aspect of respect for the public is recognizing their fundamental competence—that progressive policy experts are not the only ones who are able to understand and address complexity. Most people never set their VCRs’ clocks because they felt no need to, not because they were unable to figure out how to do so. Most people choose not to master the intricacies of public policy. But this is not because the progressive expert class is uniquely able to do so. It is—as Wu notes, that most people do not have the unlimited time or attention that would be needed to do so—time and attention that is afforded to him by his social class.

Wu’s assertion that the public “would prefer that the government solve problems for them” carries echoes of Louis Brandeis, who famously said of consumers that they were “servile, self-indulgent, indolent, ignorant.” Such a view naturally gives rise to Wu’s assumption that the public wants the government to solve problems for them. It assumes that they are unable to solve those problems on their own.

But what Brandeis and progressives cast in his mold attribute to servile indolence is more often a reflection that hoi polloi simply do not have the same concerns as Wu’s progressive expert class. If they had the time to care about the issues Wu would devote his government to, they could likely address them on their own. The fact that they don’t is less a reflection of the public’s ability than of its priorities.

II. Jobs had no monopoly on simplicity

There is another aspect to Wu’s appeal to simplicity in design that is, again, captured well in his invocation of Steve Jobs. Jobs was exceptionally successful with his minimalist, simple designs. He made a fortune for himself and more for Apple. His ideas made Apple one of the most successful companies, with one of the largest user bases, in the history of the world.

Yet many people hate Apple products. Some of these users prefer to have more complex, customizable devices—perhaps because they have particularized needs or perhaps simply because they enjoy having that additional control over how their devices operate and the feeling of ownership that that brings. Some users might dislike Apple products because the interface that is “intuitive” to millions of others is not at all intuitive to them. As trivial as it sounds, most PC users are accustomed to two-button mice—transitioning to Apple’s one-button mouse is exceptionally  discomfitting for many of these users. (In fairness, the one-button mouse design used by Apple products is not attributable to Steve Jobs.) And other users still might prefer devices that are simple in other ways, so are drawn to other products that better cater to their precise needs.

Apple has, perhaps, experienced periods of market dominance with specific products. But this has never been durable—Apple has always faced competition. And this has ensured that those parts of the public that were not well-served by Jobs’s design choices were not bound to use them—they always had alternatives.

Indeed, that is the redeeming aspect of the Theranos story: the market did what it was supposed to. While too many consumers may have been harmed by Holmes’ charlatan business practices, the reality is that once she was forced to bring the company’s product to market it was quickly outed as a failure.

This is how the market works. Companies that design good products, like Apple, are rewarded; other companies then step in to compete by offering yet better products or by addressing other segments of the market. Some of those companies succeed; most, like Theranos, fail.

This dynamic simply does not exist with government. Government is a policy monopolist. A simplified, streamlined, policy that effectively serves half the population does not effectively serve the other half. There is no alternative government that will offer competing policy designs. And to the extent that a given policy serves part of the public better than others, it creates winners and losers.

Of course, the right response to the inadequacy of Wu’s call for more, less complex policy is not that we need more, more complex policy. Rather, it’s that we need less policy—at least policy being dictated and implemented by the government. This is one of the stalwart arguments we free market and classical liberal types offer in favor of market economies: they are able to offer a wider range of goods and services that better cater to a wider range of needs of a wider range of people than the government. The reason policy grows complex is because it is trying to address complex problems; and when it fails to address those problems on a first cut, the solution is more often than not to build “patch” fixes on top of the failed policies. The result is an ever-growing book of rules bound together with voluminous “kludges” that is forever out-of-step with the changing realities of a complex, dynamic world.

The solution to so much complexity is not to sweep it under the carpet in the interest of offering simpler, but only partial, solutions catered to the needs of an anointed subset of the public. The solution is to find better ways to address those complex problems—and often times it’s simply the case that the market is better suited to such solutions.

III. A complexity: What does Wu think of consumer protection?

There is a final, and perhaps most troubling, aspect to Wu’s argument. He argues that respect for the public does not require “offering complete transparency and a multiplicity of choices.” Yet that is what he demands of business. As an academic and government official, Wu has been a loud and consistent consumer protection advocate, arguing that consumers are harmed when firms fail to provide transparency and choice—and that the government must use its coercive power to ensure that they do so.

Wu derives his insight that simpler-design-can-be-better-design from the success of Jobs—and recognizes more broadly that the consumer experience of products of the technological revolution (perhaps one could even call it the tech industry) is much better today because of this simplicity than it was in earlier times. Consumers, in other words, can be better off with firms that offer less transparency and choice. This, of course, is intuitive when one recognizes (as Wu has) that time and attention are among the scarcest of resources.

Steve Jobs and Elizabeth Holmes both understood that the avoidance of complexity and minimizing of choices are hallmarks of good design. Jobs built an empire around this; Holmes cost investors hundreds of millions of dollars in her failed pursuit. But while Holmes failed where Jobs succeeded, her failure was not tragic: Theranos was never the only medical testing laboratory in the market and, indeed, was never more than a bit player in that market. For every Apple that thrives, the marketplace erases a hundred Theranoses. But we do not have a market of governments. Wu’s call for policy to be more like Apple is a call for most government policy to fail like Theranos. Perhaps where the challenge is to do more complex policy simply, the simpler solution is to do less, but simpler, policy well.

Conclusion

We need less dumbing down of complex policy in the interest of simplicity; and we need leaders who are able to make citizens comfortable with and understanding of complexity. Wu is right that good policy need not be complex. But the lesson from that is not that complex policy should be made simple. Rather, the lesson is that policy that cannot be made simple may not be good policy after all.